Authors:
António Santos
1
;
João Rodrigues
1
;
2
;
Duarte Folgado
1
;
3
;
Sara Santos
3
;
Carlos Alberto Rosado Fujão
2
and
Hugo Gamboa
1
;
3
Affiliations:
1
Laboratório de Instrumentação, Engenharia Biomédica e Física da Radiação (LIBPhys-UNL), Departamento de Física, Faculdade de Ciências e Tecnologia, FCT, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal
;
2
Volkswagen Autoeuropa, Quinta da Marquesa, 2954-024 Q.ta do Anjo, Portugal
;
3
Associação Fraunhofer Portugal Research, Rua Alfredo Allen 455/461, 4200-135 Porto, Portugal
Keyword(s):
Self-Similarity Matrix, Time Series, Industry, Musculoskeletal Disorders, Inertial Sensors, Segmentation, Manufacturing, Unsupervised.
Abstract:
There is a significant interest to evaluate the exposure that operators are subjected throughout the working
day. The objective evaluation of occupational exposure with direct measurements and the need for automatic
annotation of relevant events arose. Using time series retrieved from inertial sensors, this work proposes a
method that is able to automatically: (1) detect anomalies, (2) segment the working cycles and (3) by means of
query-by-example, identify sub segments along the working cycle. In a short summary, this technique firstly
organizes the dataset provided by all inertial measurement units (IMUs) sensors placed over the dominant
upper limb. After this, it retrieves a wide variety of features to an organized matrix and then calculates the
respective self-similarity matrix (SSM). This method provides information by comparing each subsequence of
the time series with the remaining subsequences. As the identified structures will provide information about
how repetitive or ano
malous is the behaviour of the data in function of time. The results show that the presented
method is capable of identifying anomalies on this dataset with an accuracy of 82%, detect working cycles
with a duration error of about 6% of the working cycle, and has the ability to find matches of sub-sequences
of the working cycle.
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